Introduction | Motivation for Bioconductor Integration | Installation | Create SummarizedExperiment objects for ECODA | From SingleCellExperiment object | From Seurat object | From counts or frequency data frame | Visualization and quantification of sample separation | PCA | Quantifying group separation | Box and bar plots | Heatmap | Cell type correlation | Highly variable cell types | scECODA and pseudobulk | FAQ | Can ECODA be affected by batch effect? | Should I remove red blood cells, platelets and neutrophils? | How can I remove specific cell types? | References | Session Info

Introduction | Motivation for Bioconductor Integration | Installation | Create SummarizedExperiment objects for ECODA | From SingleCellExperiment object | From Seurat object | From counts or frequency data frame | Visualization and quantification of sample separation | PCA | Quantifying group separation | Box and bar plots | Heatmap | Cell type correlation | Highly variable cell types | scECODA and pseudobulk | FAQ | Can ECODA be affected by batch effect? | Should I remove red blood cells, platelets and neutrophils? | How can I remove specific cell types? | References | Session Info

Overview | Minimal Workflow | From a Count Matrix | From a Seurat Object | From a SingleCellExperiment Object | Common Use Cases | Compare Multiple Dissimilarity Methods | Evaluate Multiple Consistency Metrics | Visualize Results | Using Marker Genes Instead of HVGs | Focus on Specific Gene Sets | Interpreting Results | What Low Scores Mean | Next Steps for Low-Scoring Cell Types | Available Methods and Metrics | Dissimilarity Methods | Consistency Metrics | Tips and Best Practices | Getting Help | Session Info

Introduction | Key Features | Quick Start | Generate Example Data | Core Workflow | Step 1: Create scTypeEval Object | Step 2: Process Data | Step 3: Extract Relevant Features | Highly Variable Genes | Cell Type Marker Genes | Custom Gene Lists (Optional) | Step 4: Dimensional Reduction (Optional but Recommended) | Step 5: Compute Dissimilarity Matrices | Pseudobulk-based Distances | Wasserstein Distance | Reciprocal Classification | View Available Dissimilarity Matrices | Step 6: Compute Consistency Metrics | Compute Silhouette Scores | Compute Neighborhood Purity | Compare Multiple Metrics | Visualization | Dissimilarity Heatmap | Pseudobulk PCA | Interpretation Guidelines | Identifying Problematic Annotations | Recommendations | Session Information | References

Overview | Minimal Workflow | From a Count Matrix | From a Seurat Object | From a SingleCellExperiment Object | Common Use Cases | Compare Multiple Dissimilarity Methods | Evaluate Multiple Consistency Metrics | Visualize Results | Using Marker Genes Instead of HVGs | Focus on Specific Gene Sets | Interpreting Results | What Low Scores Mean | Next Steps for Low-Scoring Cell Types | Available Methods and Metrics | Dissimilarity Methods | Consistency Metrics | Tips and Best Practices | Getting Help | Session Info

Introduction | Key Features | Quick Start | Generate Example Data | Core Workflow | Step 1: Create scTypeEval Object | Step 2: Process Data | Step 3: Extract Relevant Features | Highly Variable Genes | Cell Type Marker Genes | Custom Gene Lists (Optional) | Step 4: Dimensional Reduction (Optional but Recommended) | Step 5: Compute Dissimilarity Matrices | Pseudobulk-based Distances | Wasserstein Distance | Reciprocal Classification | View Available Dissimilarity Matrices | Step 6: Compute Consistency Metrics | Compute Silhouette Scores | Compute Neighborhood Purity | Compare Multiple Metrics | Visualization | Dissimilarity Heatmap | Pseudobulk PCA | Interpretation Guidelines | Identifying Problematic Annotations | Recommendations | Session Information | References

Introduction | Quick start | Get some testing data | Define gene signatures | Run UCell | Pre-calculating gene rankings | Multi-core processing | Interacting with SingleCellExperiment or Seurat | Resources | References | Session Info

Introduction | Get some testing data | Define gene signatures | Run UCell on sce object | Signature smoothing | Resources | References | Session Info

Introduction | Get some testing data | Define gene signatures | Run UCell on Seurat object | Signature smoothing | Resources | References | Session Info

Introduction | Load example dataset | Parameters | Positive and negative gene sets in signatures | The maxRank parameter | Handling missing genes | Chunk size | Parallelization | Signature score smoothing | Resources | References | Session Info

Introduction | Quick start | Get some testing data | Define gene signatures | Run UCell | Pre-calculating gene rankings | Multi-core processing | Interacting with SingleCellExperiment or Seurat | Resources | References | Session Info

Introduction | Get some testing data | Define gene signatures | Run UCell on sce object | Signature smoothing | Resources | References | Session Info

Introduction | Get some testing data | Define gene signatures | Run UCell on Seurat object | Signature smoothing | Resources | References | Session Info

Introduction | Load example dataset | Parameters | Positive and negative gene sets in signatures | The maxRank parameter | Handling missing genes | Chunk size | Parallelization | Signature score smoothing | Resources | References | Session Info